#!/bin/bash

#当前路径,不需要修改
cur_path=`pwd`/../

#集合通信参数,不需要修改

export RANK_SIZE=1
export JOB_ID=10087
RANK_ID_START=0


# 数据集路径,保持为空,不需要修改
data_path=''
ckpt_path=''

#设置默认日志级别,不需要修改
export ASCEND_GLOBAL_LOG_LEVEL=3
#export ASCEND_DEVICE_ID=3

#基础参数，需要模型审视修改
#网络名称，同目录名称
Network="ASSEMBLED-CNN_ID0941_for_TensorFlow"
#训练epoch
train_epochs=1
#训练batch_size
batch_size=64
#训练step
train_steps=
#学习率
learning_rate=0.001

#维测参数，precision_mode需要模型审视修改
precision_mode="allow_mix_precision"
#维持参数，以下不需要修改
over_dump=False
data_dump_flag=False
data_dump_step="10"
profiling=False

# 帮助信息，不需要修改
if [[ $1 == --help || $1 == -h ]];then
    echo"usage:./train_performance_1p.sh <args>"
    echo " "
    echo "parameter explain:
    --precision_mode         precision mode(allow_fp32_to_fp16/force_fp16/must_keep_origin_dtype/allow_mix_precision)
    --over_dump		           if or not over detection, default is False
    --data_dump_flag		     data dump flag, default is False
    --data_dump_step		     data dump step, default is 10
    --profiling		           if or not profiling for performance debug, default is False
    --data_path		           source data of training
    -h/--help		             show help message
    "
    exit 1
fi

#参数校验，不需要修改
for para in $*
do
    if [[ $para == --precision_mode* ]];then
        precision_mode=`echo ${para#*=}`
    elif [[ $para == --over_dump* ]];then
        over_dump=`echo ${para#*=}`
        over_dump_path=${cur_path}/test/output/overflow_dump
        mkdir -p ${over_dump_path}
    elif [[ $para == --data_dump_flag* ]];then
        data_dump_flag=`echo ${para#*=}`
        data_dump_path=${cur_path}/test/output/data_dump
        mkdir -p ${data_dump_path}
    elif [[ $para == --data_dump_step* ]];then
        data_dump_step=`echo ${para#*=}`
    elif [[ $para == --profiling* ]];then
        profiling=`echo ${para#*=}`
        profiling_dump_path=${cur_path}/test/output/profiling
        mkdir -p ${profiling_dump_path}
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`

    elif [[ $para == --ckpt_path* ]];then
        ckpt_path=`echo ${para#*=}`
    fi
done

#校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1

fi

#训练开始时间，不需要修改
start_time=$(date +%s)

#进入训练脚本目录，需要模型审视修改
cd $cur_path/
for((RANK_ID=$RANK_ID_START;RANK_ID<$((RANK_SIZE+RANK_ID_START));RANK_ID++));
do
    #设置环境变量，不需要修改
    echo "Device ID: $ASCEND_DEVICE_ID"
    export RANK_ID=$RANK_ID



    #创建DeviceID输出目录，不需要修改
    if [ -d ${cur_path}/test/output/${ASCEND_DEVICE_ID} ];then
        rm -rf ${cur_path}/test/output/${ASCEND_DEVICE_ID}
        mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt
    else
        mkdir -p ${cur_path}/test/output/$ASCEND_DEVICE_ID/ckpt
    fi




    # 绑核，不需要的绑核的模型删除，需要的模型审视修改
    let a=RANK_ID*12
    let b=RANK_ID+1
    let c=b*12-1
	
	
    #执行训练脚本，以下传参不需要修改，其他需要模型审视修改

    python3 ./code/main_classification.py \
            --dataset_name=food101 \
            --data_dir=${data_path}/TFRecord_food101 \
            --model_dir=./temp \
            --pretrained_model_checkpoint_path=${data_path}/food_ckpt \
            --num_gpus=1 \
            --mixup_type=1 \
            --autoaugment_type=good \
            --label_smoothing=0.1 \
            --resnet_version=2 \
            --resnet_size=50 \
            --anti_alias_filter_size=3 \
            --anti_alias_type=sconv \
            --use_sk_block=True \
            --use_dropblock=True \
            --dropblock_kp="0.9,0.7" \
            --batch_size=64 \
            --preprocessing_type=imagenet_224_256a \
            --base_learning_rate=0.001 \
            --learning_rate_decay_type=cosine \
            --lr_warmup_epochs=5 \
            --train_epochs=${train_epochs} \
            --bn_momentum=0.966 \
            --weight_decay=0.00001 \
            --keep_checkpoint_max=0 \
            --ratio_fine_eval=1.0 \
            --epochs_between_evals=1 \
            --clean \
            --train_regex=train-00000-of-00128 \
            --val_regex=validation-00000-of-00016 > ${cur_path}test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &


done 
wait

#训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"
#输出性能FPS，需要模型审视修改
TrainingTime1=`grep "Perf:" $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk -F 'Perf:' 'END {print $2}'|sed 's/ //g'`
step1=`grep "global_step = " $cur_path/test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log |awk 'END {print $9}'|sed 's/://g'`
step=`expr ${step1} + 1`
TrainingTime=`awk 'BEGIN {printf "%.2f\n",'${TrainingTime1}'/'${step}'}'`

#性能看护结果汇总
#训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'

##获取性能数据，不需要修改
#吞吐量
ActualFPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${TrainingTime}'}'`

#模型精度
train_accuracy=`grep 'accuracy = ' $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $12}'|sed 's/,//g'`

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
grep 'Saving dict for global step' $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk  '{print $24}'  > $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}' $cur_path/test/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" > $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainingTime = ${TrainingTime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log